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Record W3164467933 · doi:10.1016/j.cjca.2021.05.006

Increased Expression of Proatherogenic Proteins in Immune Cell Subtypes in Tobacco Cigarette Smokers But Not in Electronic Cigarette Vapers

2021· article· en· W3164467933 on OpenAlexvenueno aff
Theodoros Kelesidis, Yuyan Zhang, Elizabeth Tran, Grace Sosa, Holly R. Middlekauff

Bibliographic record

VenueCanadian Journal of Cardiology · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsnot available
FundersClinical and Translational Science Institute, University of California, Los AngelesNational Center for Advancing Translational SciencesNational Institute of Allergy and Infectious DiseasesNational Institute on AgingOffice of the President, University of CaliforniaUniversity of California, Los AngelesNational Institutes of HealthCenter for AIDS Research, University of WashingtonTobacco-Related Disease Research Program
KeywordsInflammasomeMedicineTLR4Oxidative stressImmune systemNicotineReceptorImmunologySignallingInflammationCell biologyInternal medicineBiology

Abstract

fetched live from OpenAlex

It is unclear how oxidative stress triggered by smoking and vaping may alter specific immune cell subsets. In this study, we showed that tobacco cigarette smoking, but not electronic-cigarette vaping, is associated with increased expression of major proteins in the toll-like receptor 4 (TLR4) inflammasome-interleukin (IL)-6 signalling axis in monocyte subtypes and T cells. TLR4 senses oxidative stress in immune cells caspase-1 is a key protein of inflammasome activation, and IL-6R-α is the receptor for IL-6 that drives proatherogenic IL-6 signalling. These findings implicate the non-nicotine, pro-oxidant toxicants in tobacco cigarette smoke as instigators of increased expression of key proteins in the TLR4-inflammasome-IL-6 axis that contribute to atherogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.193
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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Same venueCanadian Journal of CardiologySame topicInflammasome and immune disordersFrench-language works237,207